{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 载入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12500/12500 [00:49<00:00, 251.57it/s]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "n = 25000\n",
    "width = 299\n",
    "\n",
    "X = np.zeros((n, width, width, 3), dtype=np.uint8)\n",
    "y = np.zeros((n,), dtype=np.uint8)\n",
    "\n",
    "for i in tqdm(range(n/2)):\n",
    "    X[i] = cv2.resize(cv2.imread('train/cat.%d.jpg' % i), (width, width))\n",
    "    X[i+n/2] = cv2.resize(cv2.imread('train/dog.%d.jpg' % i), (width, width))\n",
    "\n",
    "y[n/2:] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 提取特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import *\n",
    "from keras.models import *\n",
    "from keras.applications import *\n",
    "from keras.optimizers import *\n",
    "from keras.applications.inception_v3 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_features(MODEL, data=X):\n",
    "    cnn_model = MODEL(include_top=False, input_shape=(width, width, 3), weights='imagenet')\n",
    "    \n",
    "    inputs = Input((width, width, 3))\n",
    "    x = inputs\n",
    "    x = Lambda(preprocess_input, name='preprocessing')(x)\n",
    "    x = cnn_model(x)\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    cnn_model = Model(inputs, x)\n",
    "\n",
    "    features = cnn_model.predict(data, batch_size=64, verbose=1)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000/25000 [==============================] - 134s   \n",
      "25000/25000 [==============================] - 200s   \n"
     ]
    }
   ],
   "source": [
    "inception_features = get_features(InceptionV3, X)\n",
    "xception_features = get_features(Xception, X)\n",
    "features = np.concatenate([inception_features, xception_features], axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 融合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 20000 samples, validate on 5000 samples\n",
      "Epoch 1/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0830 - acc: 0.9754 - val_loss: 0.0352 - val_acc: 0.9960\n",
      "Epoch 2/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0264 - acc: 0.9923 - val_loss: 0.0321 - val_acc: 0.9946\n",
      "Epoch 3/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0224 - acc: 0.9928 - val_loss: 0.0241 - val_acc: 0.9952\n",
      "Epoch 4/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0195 - acc: 0.9942 - val_loss: 0.0183 - val_acc: 0.9958\n",
      "Epoch 5/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0176 - acc: 0.9946 - val_loss: 0.0379 - val_acc: 0.9892\n",
      "Epoch 6/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0175 - acc: 0.9942 - val_loss: 0.0210 - val_acc: 0.9948\n",
      "Epoch 7/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0157 - acc: 0.9953 - val_loss: 0.0206 - val_acc: 0.9950\n",
      "Epoch 8/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0159 - acc: 0.9954 - val_loss: 0.0189 - val_acc: 0.9952\n",
      "Epoch 9/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0147 - acc: 0.9954 - val_loss: 0.0230 - val_acc: 0.9940\n",
      "Epoch 10/10\n",
      "20000/20000 [==============================] - 0s - loss: 0.0135 - acc: 0.9959 - val_loss: 0.0168 - val_acc: 0.9954\n"
     ]
    }
   ],
   "source": [
    "inputs = Input(features.shape[1:])\n",
    "x = inputs\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(1, activation='sigmoid')(x)\n",
    "model = Model(inputs, x)\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "h = model.fit(features, y, batch_size=128, epochs=10, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 217)\">\n",
       "<title>G</title>\n",
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       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"62.5\" y=\"-185.8\">input_5: InputLayer</text>\n",
       "<polyline fill=\"none\" points=\"125,-166.5 125,-212.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"152.5\" y=\"-197.3\">input:</text>\n",
       "<polyline fill=\"none\" points=\"125,-189.5 180,-189.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"152.5\" y=\"-174.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"180,-166.5 180,-212.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"224.5\" y=\"-197.3\">(None, 4096)</text>\n",
       "<polyline fill=\"none\" points=\"180,-189.5 269,-189.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"224.5\" y=\"-174.3\">(None, 4096)</text>\n",
       "</g>\n",
       "<!-- 139744776582800 -->\n",
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       "<polygon fill=\"none\" points=\"0,-83.5 0,-129.5 269,-129.5 269,-83.5 0,-83.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"62.5\" y=\"-102.8\">dropout_1: Dropout</text>\n",
       "<polyline fill=\"none\" points=\"125,-83.5 125,-129.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"152.5\" y=\"-114.3\">input:</text>\n",
       "<polyline fill=\"none\" points=\"125,-106.5 180,-106.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"152.5\" y=\"-91.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"180,-83.5 180,-129.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"224.5\" y=\"-114.3\">(None, 4096)</text>\n",
       "<polyline fill=\"none\" points=\"180,-106.5 269,-106.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"224.5\" y=\"-91.3\">(None, 4096)</text>\n",
       "</g>\n",
       "<!-- 139740574507664&#45;&gt;139744776582800 -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>139740574507664-&gt;139744776582800</title>\n",
       "<path d=\"M134.5,-166.366C134.5,-158.152 134.5,-148.658 134.5,-139.725\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"138,-139.607 134.5,-129.607 131,-139.607 138,-139.607\" stroke=\"black\"/>\n",
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       "<!-- 139740574507600 -->\n",
       "<g class=\"node\" id=\"node3\"><title>139740574507600</title>\n",
       "<polygon fill=\"none\" points=\"11.5,-0.5 11.5,-46.5 257.5,-46.5 257.5,-0.5 11.5,-0.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"62.5\" y=\"-19.8\">dense_1: Dense</text>\n",
       "<polyline fill=\"none\" points=\"113.5,-0.5 113.5,-46.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"141\" y=\"-31.3\">input:</text>\n",
       "<polyline fill=\"none\" points=\"113.5,-23.5 168.5,-23.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"141\" y=\"-8.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"168.5,-0.5 168.5,-46.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"213\" y=\"-31.3\">(None, 4096)</text>\n",
       "<polyline fill=\"none\" points=\"168.5,-23.5 257.5,-23.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"213\" y=\"-8.3\">(None, 1)</text>\n",
       "</g>\n",
       "<!-- 139744776582800&#45;&gt;139740574507600 -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>139744776582800-&gt;139740574507600</title>\n",
       "<path d=\"M134.5,-83.3664C134.5,-75.1516 134.5,-65.6579 134.5,-56.7252\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"138,-56.6068 134.5,-46.6068 131,-56.6069 138,-56.6068\" stroke=\"black\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f17e12d1b90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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FmudgWRVvrNrDil0HKausaTjMqtPpJc6JjXSbYKxnF4b4h1kOSk8gLqodxTy2DVsXmUVv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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17e2d19d50>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 264,
       "width": 614
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(h.history['loss'])\n",
    "plt.plot(h.history['val_loss'])\n",
    "plt.legend(['loss', 'val_loss'])\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(h.history['acc'])\n",
    "plt.plot(h.history['val_acc'])\n",
    "plt.legend(['acc', 'val_acc'])\n",
    "plt.ylabel('acc')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 在测试集上预测并提交到 kaggle 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12500/12500 [00:24<00:00, 502.62it/s]\n"
     ]
    }
   ],
   "source": [
    "n = 12500\n",
    "X_test = np.zeros((n, width, width, 3), dtype=np.uint8)\n",
    "\n",
    "for i in tqdm(range(n)):\n",
    "    X_test[i] = cv2.resize(cv2.imread('test/%d.jpg' % (i+1)), (width, width))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12500/12500 [==============================] - 67s    \n",
      "12500/12500 [==============================] - 100s   \n"
     ]
    }
   ],
   "source": [
    "inception_features = get_features(InceptionV3, X_test)\n",
    "xception_features = get_features(Xception, X_test)\n",
    "features_test = np.concatenate([inception_features, xception_features], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(features_test, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('sample_submission.csv')\n",
    "df['label'] = y_pred.clip(min=0.005, max=0.995)\n",
    "df.to_csv('pred.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
